生物信息学在分子流行病学中的应用

张鼎; 赵亚双

doi:10.16462/j.cnki.zhjbkz.2021.01.005

生物信息学在分子流行病学中的应用

doi: 10.16462/j.cnki.zhjbkz.2021.01.005

张鼎,
赵亚双^,

150081 哈尔滨，哈尔滨医科大学公共卫生学院流行病学教研室

详细信息

通讯作者:
赵亚双，E-mail：zhao_yashuang@263.net

中图分类号: R181.3
计量
- 文章访问数: 2346
- HTML全文浏览量: 1011
- PDF下载量: 475
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-20
- 修回日期: 2020-12-26
- 刊出日期: 2021-01-10

Applications of bioinformatics in molecular epidemiology

ZHANG Ding,
ZHAO Ya-shuang^,

Department of Epidemiology, School of Public Health, Harbin Medical University, Harbin 150081, China

More Information

Corresponding author: ZHAO Ya-shuang, E-mail: zhao_yashuang@263.net

摘要

摘要: 分子流行病学主要是从分子水平阐明疾病发生、发展规律及其影响因素，其研究首先必须确定生物标志物。生物信息学作为一门分析生物数据的工具学科，可以分析和整合基因组学、转录组学、表观组学及蛋白组学等标志物的高通量数据。生物信息学在流行病学筛选及研究疾病易感性、病因探索、疾病诊断和预后等标志物方面发挥了重要作用。本文就生物信息学在分子流行病学研究中发挥的作用进行综述。
- 分子流行病学 /
- 生物信息学 /
- 高通量数据
Abstract: Molecular epidemiology mainly studies the occurrence and development of diseases and their influencing factors based on the molecular level. The primary aspect of molecular epidemiological research is based on identifying biomarkers. Bioinformatics, an instrumental discipline of analyzing biology data, can combines and analyses high-throughput data of genomics, transcriptomics, epigenomics and proteomics. Bioinformatics plays an important role in epidemiological screening and researching biomarkers for disease susceptibility, cause exploration, disease diagnosis and prognosis and others. The purpose of this review was to provide an overview of applications of bioinformatics in molecular epidemiology.
- Molecular epidemiology /
- Bioinformatics /
- High-throughput data

HTML全文

参考文献(35)

[1]	李霞, 雷健波.生物信息学[M].第2版.北京:人民卫生出版社, 2015. Li X, Lei JB. Bioinformatics[M]. 2nd ed. Beijing: People's Medical Publishing House, 2015.
[2]	詹思延.流行病学[M].第7版.北京:人民卫生出版社, 2012. Zhan SY. Epidemiology[M]. 7th ed. Beijing: People's Medical Publishing House, 2012.
[3]	王志富.基于候选基因策略的脓毒症遗传易感性研究[D].北京: 北京协和医学院, 2012. Wang ZF. Study on genetic susceptibility to sepsis based on candidate gene strategy[D]. Beijing: Peking Union Medical College, 2012.
[4]	Hogeweg P. The roots of bioinformatics in theoretical biology[J]. PLoS Comput Biol, 2011, 7(3):e1002021. DOI: 10.1371/journal.pcbi.1002021.
[5]	Sayers EW, Cavanaugh M, Clark K, et al. GenBank[J]. Nucleic Acids Res, 2020, 48(D1):D84-D86. DOI: 10.1093/nar/gkz956.
[6]	UniProt Consortium. UniProt: the universal protein knowledgebase in 2021[J]. Nucleic Acids Res, 2020:gkaa1100. DOI: 10.1093/nar/gkaa1100.
[7]	方积乾.卫生统计学[M].第7版北京:人民卫生出版社, 2012. Fang JQ. Health statistics[M]. 7th ed. Beijing: People's Medical Publishing House, 2012.
[8]	Carriço JA, Sabat AJ, Friedrich AW, et al. Bioinformatics in bacterial molecular epidemiology and public health: databases, tools and the next-generation sequencing revolution[J]. Euro Surveill, 2013, 18(4):20382. DOI: 10.2807/ese.18.04.20382-en.
[9]	Sabat AJ, Budimir A, Nashev D, et al. Overview of molecular typing methods for outbreak detection and epidemiological surveillance[J]. Euro Surveill, 2013, 18(4):20380. DOI: 10.2807/ese.18.04.20380-en.
[10]	Mellmann A, Harmsen D, Cummings CA, et al. Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology[J]. PLoS One, 2011, 6(7):e22751. DOI: 10.1371/journal.pone.0022751.
[11]	Askar M, Faber MS, Frank C, et al. Update on the ongoing outbreak of haemolytic uraemic syndrome due to Shiga toxin-producing Escherichia coli (STEC) serotype O104, Germany, May 2011[J]. Euro Surveill, 2011, 16(22):19883. DOI: 10.2807/ese.16.22.19883-en.
[12]	Frank C, Werber D, Cramer JP, et al. Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany[J]. N Engl J Med, 2011, 365(19):1771-1780. DOI: 10.1056/NEJMoa1106483.
[13]	Mellmann A, Bielaszewska M, Köck R, et al. Analysis of collection of hemolytic uremic syndrome-associated enterohemorrhagic Escherichia coli[J]. Emerg Infect Dis, 2008, 14(8):1287-1290. DOI: 10.3201/eid1408.071082.
[14]	Tang XL, Wu CC, Li X, et al. On the origin and continuing evolution of SARS-CoV-2[J]. Natl Sci Rev, 2020, 7(6):1012-1023. DOI: 10.1093/nsr/nwaa036.
[15]	Hoffmann M, Luo Y, Monday SR, et al. Tracing Origins of the Salmonella Bareilly Strain Causing a Food-borne Outbreak in the United States[J]. J Infect Dis, 2016, 213(4):502-508. DOI: 10.1093/infdis/jiv297.
[16]	Nightingale K, Lin KM, Ravenhill BJ, et al. High-Definition Analysis of Host Protein Stability during Human Cytomegalovirus Infection Reveals Antiviral Factors and Viral Evasion Mechanisms[J]. Cell Host Microbe, 2018, 24(3):447-460. DOI: 10.1016/j.chom.2018.07.011.
[17]	Collaborators GBD 2017 DALYs and HALE. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2018, 392(10159):1859-1922. DOI: 10.1016/s0140-6736(18)32335-3.
[18]	Yamada Y, Kato K, Oguri M, et al. Identification of 13 novel susceptibility loci for early-onset myocardial infarction, hypertension, or chronic kidney disease[J]. Int J Mol Med, 2018, 42(5):2415-2436. DOI: 10.3892/ijmm.2018.3852.
[19]	Todd JA. Etiology of type 1 diabetes[J]. Immunity, 2010, 32(4):457-467. DOI: 10.1016/j.immuni.2010.04.001.
[20]	Chen Z, Wen W, Beeghly-Fadiel A, et al. Identifying putative susceptibility genes and evaluating their associations with somatic mutations in human cancers[J]. Am J Hum Genet, 2019, 105(3):477-492. DOI: 10.1016/j.ajhg.2019.07.006.
[21]	Barbeira AN, Dickinson SP, Bonazzola R, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics[J]. Nat Commun, 2018, 9(1):1825. DOI: 10.1038/s41467-018-03621-1.
[22]	Gamazon ER, Wheeler HE, Shah KP, et al. A gene-based association method for mapping traits using reference transcriptome data[J]. Nat Genet, 2015, 47(9):1091-1098. DOI: 10.1038/ng.3367.
[23]	Gusev A, Ko A, Shi H, et al. Integrative approaches for large-scale transcriptome-wide association studies[J]. Nat Genet, 2016, 48(3):245-252. DOI: 10.1038/ng.3506.
[24]	Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets[J]. Nat Genet, 2016, 48(5):481-487. DOI: 10.1038/ng.3538.
[25]	Wu L, Shi W, Long J, et al. A transcriptome-wide association study of 229, 000 women identifies new candidate susceptibility genes for breast cancer[J]. Nat Genet, 2018, 50(7):968-978. DOI: 10.1038/s41588-018-0132-x.
[26]	Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients[J]. Nat Rev Cancer, 2011, 11(6):426-437. DOI: 10.1038/nrc3066.
[27]	Zeng C, Stroup EK, Zhang Z, et al. Towards precision medicine: advances in 5-hydroxymethylcytosine cancer biomarker discovery in liquid biopsy[J]. Cancer Commun (Lond), 2019, 39(1):12. DOI: 10.1186/s40880-019-0356-x.
[28]	Luo H, Zhao Q, Wei W, et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer[J]. Sci Transl Med, 2020, 12(524):eaax7533. DOI: 10.1126/scitranslmed.aax7533.
[29]	Hakimi AA, Reznik E, Lee CH, et al. An integrated metabolic atlas of clear cell renal cell carcinoma[J]. Cancer Cell, 2016, 29(1):104-116. DOI: 10.1016/j.ccell.2015.12.004.
[30]	Yu P, Lan H, Song X, et al. High expression of the SH3TC2-DT/SH3TC2 gene pair associated with FLT3 mutation and poor survival in acute myeloid leukemia: an integrated TCGA analysis[J]. Front Oncol, 2020, 10:829. DOI: 10.3389/fonc.2020.00829.
[31]	Wang P, Wu M, Tu Z, et al. Identification of RNA: 5-methylcytosine methyltransferases-related signature for predicting prognosis in glioma[J]. Front Oncol, 2020, 10:1119. DOI: 10.3389/fonc.2020.01119.
[32]	Zhu L, Wang H, Jiang C, et al. Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: a multicenter study[J]. EBioMedicine, 2020, 61:103023. DOI: 10.1016/j.ebiom.2020.103023.
[33]	Pich O, Muiños F, Lolkema MP, et al. The mutational footprints of cancer therapies[J]. Nat Genet, 2019, 51(12):1732-1740. DOI: 10.1038/s41588-019-0525-5.
[34]	Crotti S, Enzo MV, Bedin C, et al. Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy[J]. J Cell Physiol, 2015, 230(8):1822-1828. DOI: 10.1002/jcp.24894.
[35]	Li CX, Wheelock CE, Sköld CM, et al. Integration of multi-omics datasets enables molecular classification of COPD[J]. Eur Respir J, 2018, 51(5):1701930. DOI: 10.1183/13993003.01930-2017.